Predict method for singleRStaticCountData class

A method for predict function, works analogous to predict.glm but gives the possibility to get standard errors of mean/distribution parameters and directly get pop size estimates for new data.

Usage

# S3 method for class 'singleRStaticCountData'
predict(
  object,
  newdata,
  type = c("response", "link", "mean", "popSize", "contr"),
  se.fit = FALSE,
  na.action = NULL,
  weights,
  cov,
  ...
)

Arguments

object

an object of singleRStaticCountData class.

newdata

an optional data.frame containing new data.

type

the type of prediction required, possible values are:

• "response"– For matrix containing estimated distributions parameters.

• "link" – For matrix of linear predictors.

• "mean" – For fitted values of both Y and Y|Y>0.

• "contr" – For inverse probability weights (here named for observation contribution to population size estimate).

• "popSize" – For population size estimation. Note this results in a call to redoPopEstimation and it is usually better to call this function directly.

by default set to "response".

se.fit

a logical value indicating whether standard errors should be computed. Only matters for type in "response", "mean", "link".

na.action

does nothing yet.

weights

optional vector of weights for type in "contr", "popSize".

cov

optional matrix or function or character specifying either a covariance matrix or a function to compute that covariance matrix. By default vcov.singleRStaticCountData can be set to e.g. vcovHC.

...

arguments passed to other functions, for now this only affects vcov.singleRStaticCountData method and cov function.

Value

Depending on type argument if one of "response", "link", "mean" a matrix with fitted values and possibly standard errors if se.fit argument was set to TRUE, if type was set to "contr" a vector with inverses of probabilities, finally for "popSize" an object of class popSizeEstResults with its own methods containing population size estimation results.

Details

Standard errors are computed with assumption of regression coefficients being asymptotically normally distributed, if this assumption holds then each of linear predictors i.e. each row of =X_vlm is asymptotically normally distributed and their variances are expressed by well known formula. The mean and distribution parameters are then differentiable functions of asymptotically normally distributed variables and therefore their variances can be computed using (multivariate) delta method.

See also

redoPopEstimation() stats::summary.glm() estimatePopsize()